Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1].
Accelerated Linear Convergence of Stochastic Momentum Methods in Wasserstein Distances
Authors: Bugra Can, Mert Gurbuzbalaban, Lingjiong Zhu
ICML 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 1We also provide numerical experiments in the supplementary ο¬le to illustrate the results of Theorem 4. |
| Researcher Affiliation | Academia | 1Department of Management Science and Information Systems, Rutgers Business School, Piscataway, NJ-08854, United States of America 2Department of Mathematics, Florida State University, 1017 Academic Way, Tallahassee, FL-32306, United States of America. |
| Pseudocode | No | The paper describes optimization algorithms using mathematical equations and textual descriptions but does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described in this paper. |
| Open Datasets | No | The paper primarily presents theoretical analysis and does not provide concrete access information for a publicly available or open dataset for training. |
| Dataset Splits | No | The paper does not provide specific dataset split information needed to reproduce the data partitioning, as it focuses on theoretical analysis. |
| Hardware Specification | No | The paper is theoretical and does not provide specific hardware details used for running its experiments. |
| Software Dependencies | No | The paper is theoretical and does not provide specific ancillary software details needed to replicate the experiment. |
| Experiment Setup | No | The paper focuses on theoretical analysis and does not provide specific experimental setup details (concrete hyperparameter values, training configurations, or system-level settings) in the main text. |